This application is based on applications Nos. 9-257025 and 9-257040 filed in Japan, the contents of which are hereby incorporated by reference.
The present invention relates to an information processing apparatus capable of automatically setting the degree of relevance between keywords to be attached to objects, and also to a keyword attaching method for automatically attaching a keyword to an object to be registered in storage as well as a keyword auto-attaching apparatus using this keyword attaching method.
With the rapid popularization of computers, electronic information treated by people has been on a rapid increase in amount. These large volumes of electronic information are generally stored together with keywords, and specifying the keyword allows desired electronic information to be retrieved.
An example of the method for attaching a keyword to such electronic information is one disclosed in Japanese Patent Laid-Open Publication HEI 6-295318. In this keyword attaching method, the following procedure is taken, for example, to register image data of a human face picture with a keyword attached thereto.
Various. keyword candidates related to the human face, such as xe2x80x9clong facexe2x80x9d and xe2x80x9cround facexe2x80x9d, are registered beforehand. Also, for each keyword candidate, goodness-of-fit calculating information and a threshold value to be used in calculating the goodness of fit are registered. For example, the goodness-of-fit calculating information for a keyword candidate of xe2x80x9clong facexe2x80x9d is xe2x80x9clength-to-width ratio of profilexe2x80x9d. Next, one keyword candidate xe2x80x9clong facexe2x80x9d as well as the goodness-of-fit calculating information and threshold value associated with this keyword candidate xe2x80x9clong facexe2x80x9d are read out, and a goodness of fit is calculated based on the goodness-of-fit calculating information of the keyword candidate xe2x80x9clong facexe2x80x9d from feature quantities of image data of a target object to which a keyword is to be attached. Then, if the resulting goodness of fit is equal to or more than the threshold value of the keyword candidate xe2x80x9clong facexe2x80x9d, the keyword candidate xe2x80x9clong facexe2x80x9d is determined as the keyword to be attached to the pertinent image data. From this on, similar operation is performed for all the registered keyword candidates, by which the keyword to be attached is determined.
In this way, an objective keyword attachment is achieved without relying on the subjective mind of the registering operator.
There has also been available another keyword attaching method as disclosed in Japanese Patent Laid-Open Publication HEI 8-329096. In this keyword attaching method, a two-dimensional keyword map having two axes such as comfort-discomfort and strong-weak is defined. Then, when a keyword is registered, the keyword to be registered is placed by manual operation at a point on the keyword map. With this arrangement, in an ambiguous retrieval of registered image data, specifying a distance from a specified keyword on the axes of the keyword map allows image data to be retrieved under a retrieval condition that keywords are located within the specified distance around the position of the specified keyword on the keyword map.
However, with the keyword attaching method described in the above Japanese Patent Laid-Open Publication HEI 6-295318, the degrees of relevance between every twos of all the keyword candidates are unknown. Therefore, there is a problem that when image retrieval is done, it is impossible to do an ambiguous retrieval that one keyword is specified and image retrieval is performed based on the specified keyword and similar keywords located in the neighborhood of the specified keyword. Further, the calculation of goodness of fit based on the goodness-of-fit calculating information and the comparison of threshold values must be iterated to the number of registered keyword candidates, taking much time for the work of registering image data as another problem. Moreover, suitable goodness-of-fit calculating information and threshold values need to be registered beforehand for individual keyword candidates, which would demand troublesome, difficult work in building a keyword attaching system although the keyword attaching work itself is done automatically.
Further, with the keyword attaching method described in the above Japanese Patent Laid-Open Publication HEI 8-329096, keywords can be positioned on one two-dimensional space, and the degree of relevance between keywords can be defined. Therefore, this method is effective for keyword selection in an ambiguous retrieval as described above. However, the correspondence between a keyword and image data to which this keyword should be attached is performed by persons. Also, the placement of keywords onto the keyword map must be done by manual operation as described above, which is very troublesome as a problem.
Accordingly, an object of the present invention is to provide an information processing apparatus which is capable of automatically setting the degree of relevance between keywords.
Another object of the present invention is to provide a keyword attaching method which allows a keyword to be attached simply and automatically, and a keyword auto-attaching apparatus using this keyword attaching method.
In order to achieve the above objects, the present invention provides an information processing apparatus in which an object is registered in storage with a keyword attached thereto, the apparatus comprising
keyword space defining means for, based on a correspondence between a plurality of objects and a plurality of keywords attached to these objects, defining a keyword space for the plurality of keywords.
With this constitution, the keyword space having a scale set according to the correspondence between the plurality of objects and the plurality of keywords attached to these objects is defined. Therefore, a degree of relevance corresponding to a placement distance is set between every twos of the plurality of keywords placed on the keyword space. That is, according to this invention, degrees of relevance between keywords can be set automatically and efficiently so that the user can be freed from the troublesomeness in manually setting the degrees of relevance.
Furthermore, when a plurality of objects for acquisition of the above correspondence are set according to the field of use or the kind of use, a keyword space complying with the purpose can be built up.
Also, the present invention provides an information processing apparatus in which an object is registered in storage with a keyword attached thereto, the apparatus comprising
object space defining means for, based on a correspondence between a plurality of objects and a plurality of keywords attached to these objects, defining an object space for the plurality of objects.
With this constitution, the object space having a scale set according to the correspondence between the plurality of objects and the plurality of attached keywords is defined. Therefore, a degree of relevance corresponding to a placement distance is set between every twos of the plurality of keywords placed on the keyword space. That is, according to this invention, degrees of relevance between keywords can be set automatically and efficiently so that the user can be freed from the troublesomeness in manually setting the degrees of relevance.
Furthermore, when a plurality of objects for acquisition of the above correspondence are set according to the field of use or the kind of use, an object space complying with the purpose can be built up.
An embodiment further comprises object space defining means for, based on the correspondence, defining an object space for the plurality of objects, wherein
the defined keyword space and object space are the same meaning space.
With this constitution, because the keyword space and the object space are of the same meaning space, a keyword placed at a position on the keyword space and an object placed at a position on the object space corresponding to the foregoing position have the same meaning. Therefore, a keyword suitable for an object is present in the neighborhood of the same position on the keyword space as the position of the object on the object space.
In an embodiment, the correspondence is obtained by mathematical quantification theory class III with each keyword used as a category and with each object used as a sample.
With this constitution, only giving an example of the object to which a keyword has been attached allows the keyword space and an object space having a high correlation to be defined. Then, each keyword is placed at a position on the keyword space according to the degrees of relevance with other keywords. Likewise, each object is placed at a position on the object space according to the degrees of relevance with other objects.
An embodiment further comprises keyword registering means for, based on a position of an object the position of which on the object space is known and which is corresponded to a registration-target keyword, determining the position of the registration-target keyword on the keyword space, and placing the registration-target keyword at the position on the keyword space.
With this constitution, only by associating with a registration-target keyword an object whose position on the object space has been known, the registration-target keyword is automatically placed at a proper position on the object space.
An embodiment further comprises keyword specifying means for specifying two keywords to be positioned at opposite poles; and
axis adding means for setting a new axis which interconnects the two specified keywords on the keyword space, and projecting all keywords already placed on the keyword space onto the set new axis to determine coordinates thereof on the new axis.
With this constitution, the new axis on the keyword space with which two keywords specified by the user are made to be positioned at both ends of a meaning scale is set. Thus, the new axis having a meaning understandable for the user is set on the keyword space.
Thus, in this information processing apparatus, two keywords to be positioned at opposite poles are specified by the keyword specifying means, and the new axis which connects the two keywords to each other is newly set on the keyword space by the axis adding means so that all the keywords already placed are projected onto the new axis. Thus, the new axis having a new evaluation scale specified by the user can be additionally set in the keyword space.
Furthermore, the present invention provides a computer program product in a memory which has stored therein a program for, based on a correspondence between a plurality of objects and a plurality of keywords attached to these objects, defining a keyword space for the plurality of keywords.
With this constitution, the keyword space having a scale set according to the correlation between the plurality of objects and the plurality of attached keywords is defined. Therefore, a degree of relevance corresponding to a placement distance is set between every twos of the plurality of keywords placed on the keyword space. That is, according to this invention, degrees of relevance between keywords can be set automatically and efficiently so that the user can be freed from the troublesomeness in manually setting the degrees of relevance.
Further, the present invention provides a computer program product in a memory which has stored therein a program for, based on a correspondence between a plurality of objects and a plurality of keywords attached to these objects, defining an object space for the plurality of objects.
With this constitution, the object space having a scale set according to the correspondence between the plurality of objects and the plurality of attached keywords is defined. Therefore, a degree of relevance corresponding to a placement distance is set between every twos of the plurality of objects placed on the object space.
A computer program product of an embodiment comprises a program stored therein for defining an object space for the plurality of objects, which is the same meaning space as the defined keyword space, based on the correspondence.
With this constitution, because the keyword space and the object space is of the same meaning space, a keyword placed at a position on the keyword space and an object placed at a position on the object space corresponding to the foregoing position have the same meaning. Therefore, a keyword suitable for one object is present in the neighborhood of the same position on the keyword space as the position of the object on the object space.
In an embodiment, the correspondence is obtained by mathematical quantification theory class III with each keyword used as a category and with each object used as a sample.
With this constitution, only by giving an example of the object to which a keyword has been attached, the keyword space and the object space having a high correlation is defined. Then, each keyword is placed at a position on the keyword space according to the degrees of relevance with other keywords. Likewise, each object is placed at a position on the object space according to the degrees of relevance with other objects.
A computer program product of an embodiment comprises a program stored therein for, based on a position of an object the position of which on the object space is known and which is corresponded to a registration-target keyword, determining the position of the registration-target keyword on the keyword space, and positioning the registration-target keyword at the position on the keyword space.
With this constitution, only by associating with a registration-target keyword an object whose position on the object space has been known, the registration-target keyword is automatically placed at a proper position on the object space.
A computer program product of an embodiment comprises a program for setting on the keyword space a new axis which interconnects two specified keywords to be positioned at opposite poles with each other, and projecting all keywords already placed on the keyword space onto the set new axis to determine coordinates thereof on the new axis.
With this constitution, the new axis on the keyword space with which two keywords specified by the user are made to be positioned at both ends of a meaning scale is set. Thus, the new axis having a meaning understandable for the user is set on the keyword space.
The present invention also provides a keyword attaching method for automatically attaching a keyword to an object to be registered in storage, comprising
preparing a two- or more-dimensional meaning space in which a plurality of objects and keywords attached to these objects are placed;
extracting feature quantities of a registration-target object;
determining a position of the registration-target object on the meaning space based on the feature quantities; and
selecting a keyword positioned in a neighborhood of the position of the registration-target object and attaching the keyword to the registration-target object.
With this constitution, the two- or more-dimensional meaning space in which a plurality of objects and keywords attached to these objects are placed can be obtained easily by a multivariate analysis of mathematical quantification theory class III or the like. Therefore, only by giving examples of a plurality of objects to which keywords have been attached, a keyword suitable for a registration-target object is selected and attached easily and automatically. Therefore, the user can be freed from very troublesome work of selecting and attaching a suitable keyword in the registration of an object, so that the object registering work can be achieved efficiently.
Further, with the preparation of a meaning space in which a plurality of objects and keywords attached to these objects are placed, according to the field of use or the kind of use, a suitable keyword can be attached according to the field of use or the kind of use.
An embodiment further comprises performing a canonical correlation analysis on positions on the meaning space and feature quantities relating to a plurality of objects, thereby determining a correlation between the positions on the meaning space and the feature quantities relating to the plurality of objects; and
determining the position of the registration-target object on the meaning space based on the feature quantities of the registration-target object by using the correlation.
With this constitution, by using the correlation between positions on the meaning space and feature quantities relating to the plurality of objects acquired by canonical correlation analysis, the correct position of the registration-target object on the meaning space is determined based on the feature quantities of the registration-target object. Thus, a keyword more suitable for the registration-target object can be attached.
In a method of an embodiment, the meaning space is divisionally prepared in an object-use meaning space in which the plurality of objects are placed, and a keyword-use meaning space which has the same spatial structure as the object-use meaning space and in which the keywords attached to the plurality of objects are placed, the method further comprising;
selecting n (n: integer) objects positioned in a neighborhood of the position of the registration-target object on the object-use meaning space; and
selecting a keyword positioned in a neighborhood of the registration-target object based on the positions of the individual n selected objects on the object-use meaning space and by using the keyword-use meaning space.
With this constitution, by using the object-use meaning space and the keyword-use meaning space having the same spatial structure, a keyword positioned in the neighborhood of the position of the registration-target object is easily selected.
In a method of an embodiment, the meaning space is divisionally prepared in an object-use meaning space in which the plurality of objects are placed, and a keyword-use meaning space which has the same spatial structure as the object-use meaning space and in which the keywords attached to the plurality of objects are placed, the method further comprising:
selecting n objects positioned in a neighborhood of the position of the registration-target object on the object-use meaning space;
determining a focused position on the object-use meaning space based on the positions of the selected n objects on the object-use meaning space; and
selecting a keyword positioned in a neighborhood of the registration-target object based on the focused positions and by using the keyword-use meaning space.
With this constitution, by using the object-use meaning space and the keyword-use meaning space having the same spatial structure, a keyword positioned in the neighborhood of the position of the registration-target object is easily selected.
Further, the present invention provides a keyword auto-attaching apparatus for automatically attaching a keyword to an object to be registered in storage, comprising:
meaning space storing means in which information as to a two- or more-dimensional meaning space in which a plurality of objects and keywords attached to these objects are placed is stored;
feature quantity extracting means for extracting feature quantities of a registration-target object;
object position calculating means for, based on the feature quantities, determining a position of the registration-target object on the meaning space;
keyword selecting means for selecting keywords positioned in a neighborhood of the position of the registration-target object out of the keywords placed on the meaning space; and
keyword attaching means for attaching the selected keywords to the registration-target object.
With this constitution, the two- or more-dimensional meaning space to be stored in the meaning space storage can be easily obtained based on the plurality of objects and keywords attached to these objects by a multivariate analysis of mathematical quantification theory class III or the like. Therefore, only by preparing a plurality of objects to which keywords have been attached, and based on the position of a registration-target object on the meaning space, a keyword suitable for the registration-target object is selected easily and automatically from among the keywords placed on the meaning space. As a result, the user can be freed from very troublesome work of selecting and attaching a suitable keyword in the registration of an object, so that the object registering work can be achieved efficiently.
Further, when information as to the meaning space in which a plurality of objects and keywords attached to these objects according to the field of use or the kind of use have been stored is stored in the meaning space storage means, then a suitable keyword according to the field of use or the kind of use can be attached.
An embodiment further comprises correlation calculating means for performing a canonical correlation analysis on positions on the meaning space and feature quantities relating to a plurality of objects, thereby determining a correlation between positions on the meaning space and feature quantities relating to the plurality of objects, wherein
the object position calculating means determines the position of the registration-target object on the meaning space based on the feature quantities of the registration-target object, by using the correlation.
With this constitution, by using the correlation between positions on the meaning space and feature quantities relating to the plurality of objects acquired by canonical correlation analysis, and based on the feature quantities of a registration-target object, the correct position of the registration-target object on the meaning space can be determined. As a result, a keyword more suitable for the registration-target object can be attached.
In an embodiment, information as to the meaning space is divisionally stored in an object-use meaning space in which the plurality of objects are placed, and a keyword-use meaning space which has the same spatial structure as the object-use meaning space and in which the keywords attached to the plurality of objects are placed, and wherein
the keyword selecting means comprises:
neighboring object extracting means for extracting n (n: integer) objects positioned in a neighborhood of the position of the registration-target object on the object-use meaning space; and
neighboring keyword extracting means for extracting keywords positioned in a neighborhood of the registration-target object based on the positions of the individual n extracted objects on the object-use meaning space, and by using the keyword-use meaning space.
With this constitution, by associating the object-use meaning space and the keyword-oriented meaning space having the same spatial structure with each other, a keyword positioned in the neighborhood of the position of the registration-target object can be easily selected.
In an embodiment, information as to the meaning space is divisionally stored in an object-use meaning space in which the plurality of objects are placed, and a keyword-use meaning space which has the same spatial structure as the object-use meaning space and in which the keywords attached to the plurality of objects are placed, and wherein
the keyword selecting means comprises:
neighboring object extracting means for extracting n objects positioned in a neighborhood of the position of the registration-target object on the object-use meaning space;
focused position calculating means for determining a focused position on the object-use meaning space based on the positions of the extracted n objects on the object-use meaning space; and
neighboring keyword extracting means for extracting keywords positioned in a neighborhood of the registration-target object based on the focused positions and by using the keyword-use meaning space.
With this constitution, by associating the object-use meaning space and the keyword-use meaning space having the same spatial structure with each other, a keyword positioned in the neighborhood of the position of the registration-target object can be easily selected.
The present invention also provides a computer program product in a memory, comprising a program for
extracting feature quantities of a registration-target object;
determining, based on the feature quantities, a position of the registration-target object on a two- or more-dimensional meaning space in which a plurality of objects and keywords attached to these objects are placed; and
selecting keywords positioned in a neighborhood of the position of the registration-target object out of the keywords placed on the meaning space, and attaching the keywords to the registration-target object.
With this constitution, the position of a registration-target object in the meaning space in which a plurality of objects and keywords attached to these objects have been placed is determined based on the feature quantities of the registration-target object. As a result, a keyword positioned in the neighborhood of the position of the registration-target object on the meaning space can be easily obtained as a keyword suitable for the registration-target object.
A computer program product of an embodiment comprises a program for
performing a canonical correlation analysis on positions on the meaning space and feature quantities relating to a plurality of objects, thereby determining a correlation between positions on the meaning space and feature quantities relating to the plurality of objects; and
determining the position of the registration-target object on the meaning space based on the feature quantities of the registration-target object, by using the correlation.
With this constitution, when the position of the registration-target object on the meaning space is determined based on the feature quantities of the registration-target object, the correlation acquired by canonical correlation analysis is used. Thus, based on the feature quantities of the registration-target object, the correct position of the registration-target object on the meaning space is determined.
It is noted here that the term xe2x80x9cobjectxe2x80x9d refers to electronic information (for example, text data or image data or audio data) which should be the major body to be saved by an information processing apparatus like the present retrieval apparatus, or to holders or the like into which those data have been integrated.